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All Journal Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang J-SAKTI (Jurnal Sains Komputer dan Informatika) JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal ICT : Information Communication & Technology JSAI (Journal Scientific and Applied Informatics) Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Respati G-Tech : Jurnal Teknologi Terapan JIKA (Jurnal Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA Journal of Electrical Engineering and Computer (JEECOM) Information System Journal (INFOS) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Comprehensive Science Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Bangkit Indonesia Jurnal Pendidikan Indonesia (Japendi) Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science International Journal of Information Engineering and Science
DHANI ARIATMANTO
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Social Sciences Transportation Other

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PENERAPAN MEDIA AJAR TENTANG PROFESI KERJA BERBASIS DEKSTOP MENGGUNAKAN TEKNOLOGI AUGMENTED REALITY SEBAGAI MOTIVASI BELAJAR UNTUK ANAK-ANAK USIA DINI (STUDI KASUS TK BUDI MULIA II YOGYAKARTA) Ariatmanto, Dhani; Slameto, Andika Agus; Sulistiyono, Mulia
Jurnal Teknologi Informasi RESPATI Vol 11, No 33 (2016)
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.205 KB) | DOI: 10.35842/jtir.v11i33.107

Abstract

AbstrakPerkembangan teknologi IT sebagai alat bantu media ajar menjadi daya tarik dalam memotivasi anak-anak khususnya usia dini untuk mempelajari sesuatu. Penerapan teknologi dalam pembelajaran membantu guru-guru untuk menguasai dan memanfaatkan teknologi dalam proses mengajar didalam kelas.TK Budi Mulia II Yogyakarta merupakan tempat belajar dan menuntut ilmu bagi anak-anak usia dini. Pemanfaatan teknologi komputer dan proyektor dalam pengajarannya pun sudah digunakan dalam kelas. Hal ini menjadi salah satu daya tarik untuk peserta didik apabila dalam prosesnya digabungkan dengan teknologi Augmented Reality.Augmented Reality (selanjutnya disebut AR), adalah sebuah teknologi yang pada awal dikembangkannya (1968) memiliki lingkup utama di “visual augmentation”, penambahan objek digital dalam visualisasi. Dalam perjalanannya, teknologi AR telah berkembang pesat. Dengan peningkatan ketersediaan perangkat imaging device yang semakin murah dengan konsumsi daya yang semakin rendah, kita melihat peningkatan yang pesat dalam integrasinya dengan perangkat desktop mapun perangkat yang lain seperti tablet ataupun mobile.Dengan penggabungan teknologi Augmented reality dalam media ajar penelitian ini ingin menghasilkan permodelan 3D mengenai karakter-karakter profesi kerja baik kepolisian, dokter, pilot, antariksawan, dosen, guru, dan lain-lain. Untuk dapat meningkat motivasi peserta didik tidak hanya terimajinasikan saja namun dapat terlihat visualisasi dalam layar proyektor, sehingga menumbuhkan semangat belajar pserta didik.Kata kunci: Augmented Reality, Media Pembelajaran, Permodelan 3D
Ward and Peppard Method Approach for Strategic Planning Information Systems XYZ Training Center Quratul Ain; Norlaila Norlaila; Silvi Agustanti Bambang; Sukoco Sukoco; Dhani Ariatmanto; Adrianto M. Wijaya
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 8 No 4 (2021): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v8i4.1174

Abstract

ELTIBIZ Training Center is one of the training institutions engaged in non-formal education that has used information systems and information technology to make organizational performance more effective, efficient and increase competitiveness. The IS/IT strategy is needed to facilitate the management of information by the organization in winning the competition with competitors. In this study, IS/IT strategic planning uses the Ward and Peppard method starting from the process of analyzing the condition of the external and internal business environment, as well as the external and internal IS/IT environment. The analysis process uses SWOT analysis techniques, Value Chain analysis, Porter's Five Forces analysis, technology trend analysis and Mcfarlan's Strategic Grid matrix. The IS/IT strategic plan produced in this study includes an IS strategy in the form of a portofolio of future applications that can support business processes, an IS/IT management strategy in the form of a proposed system application development. The IS/IT strategic plan is written into an information system development roadmap as an implementation reference for the ELTIBIZ Training Center in the future whose implementation plan will be carried out within the next 5 (five) years. Keyword : Strategic Plan, Information System, Ward and Peppard, SWOT, Value Chain.
Perbandingan Metode Word Embedding Untuk Analisis Sentimen Pada Data Ulasan Marketplace Nur’aini; Arfian Yogi Ferianto; Dhani Ariatmanto; Mardhiya Hayaty; Norhikmah .
Jurnal ICT: Information Communication & Technology Vol. 22 No. 2 (2022): JICT-IKMI, December 2022
Publisher : LPPM STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Marketplace is a platform for buying and selling goods online, one of whichis shopee. The platform provides a lot of short text data about reviews of various products being sold. Therefore, sentiment analysis is carried out for the classification of reviews by taking into account the factors in the sentiment object.In sentiment analysis, there is a more advanced method, namely using word embedding, word representation in vectors, many researchers have used this method in their research. Therefore, this study uses review data obtained from the shopee marketplace for sentiment analysis.In this study, data is classified using Long Short Term Memory (LSTM).Reviews that are classified will have 2 labels namely positive and negative. Thisstudy aims to determine the final accuracy and vocabulary generated by word embedding which is classified using LSTM in analyzing sentiment in Indonesian shopee reviews.Word embedding methods used are Word2Vec and Global Vector (Glove).This study uses a dataset of 10,000 to produce a vocabulary of 18004 words. From the dataset, 80% training data and 20% test data were distributed. The accuracy of the word embedding word2vec method is 83% and the word embedding Glove method gets 86% accuracy.
Penerapan model InceptionV3 dalam klasifikasi penyakit ayam Muhammad Salimy Ahsan; Kusrini Kusrini; Dhani Ariatmanto
JNANALOKA Vol. 04 No. 02 September Tahun 2023
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2023.v4-no02-55-62

Abstract

Chicken disease is one of the problems that can have a very significant impact on chicken farmers, in addition to having an impact on the farm itself, chicken disease can also have an impact on the surrounding environment. Lack of knowledge about the symptoms and diseases that occur in chickens, makes some chicken breeders treat and treat diseases in a traditional way. This method often takes a long time and is prone to errors. In this study, technology will be used to classify chicken diseases by utilizing a deep learning model from the Convolutional Neural Network (CNN) architecture, namely InceptionV3. In carrying out the process of classifying chicken diseases, using a dataset of chicken feces images with a number of 8067 Healthy, Salmonella, Coccidiosis, and Newcastle disease. In the research process, three experimental scenarios were carried out using 20 epochs, 50 epochs and 100 epochs. From the experimental results, using a value of 100 epochs produces the highest accuracy value with a value of 94.05%.
Literatur Reviu Sistematis: Identifikasi Jenis Ular Berbasis Computer Vision Putriany, Eva; Eva Putriany; Dhani Ariatmanto
JNANALOKA Vol. 05 No. 01 Maret Tahun 2024
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2024.v5-no01-43-50

Abstract

Systematic Literature Review ini bertujuan untuk mengidentifikasi algoritma-algoritma yang digunakan dalam identifikasi spesies ular yang menggunakan computer vision, mengevaluasi dataset, tingkat akurasi, faktor-faktor yang memengaruhi akurasi, dan keterbatasan yang dihadapi. Melalui tinjauan literatur sistematis, 20 paper terpilih dari tahun 2019-2023, yang didapat dari berbagai sumber literatur. Penelitian-penelitian tersebut mengeksplorasi berbagai strategi untuk mengatasi tantangan pengenalan objek ular secara otomatis, termasuk peningkatan kinerja model, eksplorasi pendekatan baru, dan penerapan solusi efektif. Hasil dari studi literatur menyoroti pentingnya pemrosesan data yang cermat, pemilihan arsitektur model yang tepat, serta penyesuaian parameter algoritma yang optimal dalam mencapai kinerja maksimal pada model-model yang dikembangkan. Beberapa peneliti juga mengemukakan keterbatasan dalam penelitiannya, seperti kualitas dan jumlah dataset, kompleksitas morfologi ular, dan variasi pose ular. Diperlukan kerja sama lintas disiplin dan berbagi pengetahuan untuk mengatasi tantangan ini dan memajukan bidang identifikasi spesies ular melalui computer vision.
PENGGABUNGAN K-NEAREST NEIGHBORS DAN LIGHTGBM UNTUK PREDIKSI DIABETES PADA DATASET PIMA INDIANS: MENGGUNAKAN PENDEKATAN EXPLORATORY DATA ANALYSIS Pramudyantoro, Arvi; Utami, Ema; Ariatmanto, Dhani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 3 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i3.4966

Abstract

Diabetes Melitus merupakan masalah kesehatan yang signifikan di seluruh dunia. Dengan menggabungkan algoritma K-Nearest Neighbors (KNN) dan Light Gradient Boosting Machine (LightGBM),penelitian ini menyajikan pendekatan baru untuk meningkatkan prediksi diabetes. Kumpulan data Indian Pima, yang terkenal dengan intrik dan signifikansinya dalam penelitian diabetes, menjadi subjek penelitian ini. Untuk menyelidiki pola dan hubungan dalam data, penelitian ini menggunakan analisis data eksploratif, atau EDA. Pra-pemrosesan data yang komprehensif, yang mencakup pengkodean, normalisasi, dan penanganan nilai yang hilang, adalah yang berikutnya. Karena KNN dan LightGBM cocok dengan fitur kumpulan data ini, maka keduanya dipilih. Performa model dioptimalkan melalui penggunaan teknik pengoptimalan seperti Pencarian Acak dan Pencarian Grid untuk mengubah hyperparameter. Metrik seperti skor F1, kurva ROC, analisis presisi-recall, dan akurasi-presisi digunakan untuk menilai model. Hasilnya menunjukkan peningkatan signifikan dalam keakuratan prediksi diabetes, yang menunjukkan bahwa penggunaan LightGBM bersama dengan KNN dan EDA secara hati-hati dapat meningkatkan akurasi prediksi. Khususnya bila dipertimbangkan dalam konteks data kesehatan yang rumit, temuan ini secara signifikan memajukan deteksi penyakit kronis. Menggunakan kumpulan data Pima Indians, algoritma KNN dan LightGBM bekerja sama untuk mencapai akurasi tertinggi sebesar 90,6%.
Detect Fake Reviews Using Random Forest and Support Vector Machine Hadi, Zulpan; Utami, Ema; Ariatmanto, Dhani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12090

Abstract

With the rapid development of e-commerce, which makes it possible to buy and sell products and services online, customers are increasingly using these online shop sites to fulfill their needs. After purchase, customers write reviews about their personal experiences, feelings and emotions. Reviews of a product are the main source of information for customers to make decisions to buy or not a product. However, reviews that should be one piece of information that can be trusted by customers can actually be manipulated by the owner of the seller. Where sellers can spam reviews to increase their product ratings or bring down their competitors. Therefore this study discusses detecting fake reviews on productreviews on Tokopedia. Where the method used is the distribution post tagging feature to perform detection. By using the post tagging feature method the distribution got 856 fake reviews and 4478 genuine reviews. In the fake reviews, there were 628 reviews written with the aim of increasing product sales or brand names from store owners, while there were 228 reviews aimed at dropping their competitors or competitors. Furthermore, the classification is carried out using the random forest algorithm model and the support vector machine. By dividing the dataset for training data by 80% while 20% for data testing. Here it is known that the support vector machine gets much higher accuracy than the random forest. The support vector machine gets an accuracy of 98% while the random forest gets an accuracy of 60%
Chicken Disease Classification Based on Inception V3 Algorithm for Data Imbalance Ahsan, Muhammad Salimy; Kusrini; Dhani Ariatmanto
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12737

Abstract

In order to supply the world's protein needs, one of the most crucial industries is the poultry business. The problem that often occurs in chicken farms is disease, and this can have a significant impact on the farm. The availability of large enough amounts of data makes it possible to carry out the process of monitoring chicken diseases using deep learning technology for the classification of chicken diseases. With the availability of large enough data, the dataset has a variety of features that cause problems with data clutter. To overcome the problem of data conflict, an oversampling technique is used to increase the sample data from the minority class so that it has the same value as the other majority classes, and the Inception-V3 algorithm is used to classify chicken diseases based on fecal images. The total number of data used was 8067, which were broken down into the following four categories: Healthy, Salmonella, Coccidiosis, and Newcastle disease. Data balancing was done using oversampling to get the total data to 10500 before the evaluation process was started. The data was distributed by splitting it by 80% of the data will be used for training, 10% for data validation, and 10% for testing. The results of the test, which employed Inception V3 without oversampling, produced the highest possible score of 94.05%.
Comparative Analysis of CNN and CNN-SVM Methods For Classification Types of Human Skin Disease Anggriandi, Dendi; Utami, Ema; Ariatmanto, Dhani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12831

Abstract

Cancer is one of the leading causes of death worldwide, with skin cancer ranking fifth. The skin, as the outermost organ of the body, is susceptible to various diseases, and accurate diagnosis is crucial for effective treatment. However, limited access to dermatologists and expensive skin biopsies poses challenges in achieving efficient diagnosis. Therefore, it is important to develop a system that can assist in efficiently classifying skin diseases to overcome these limitations. In the field of skin disease classification, Machine Learning and Deep Learning methods, especially Convolutional Neural Network (CNN), have demonstrated high accuracy in medical image classification. CNN's advantage lies in its ability to automatically and deeply extract features from skin images. The combination of CNN and Support Vector Machine (SVM) offers an interesting approach, with CNN used for feature extraction and SVM as the classification algorithm. This research compares two classification methods: CNN with MobileNet architecture and CNN-SVM with various kernel types to classify human skin diseases. The dataset consists of seven classes of skin diseases with a total of 21.000 images. The results of the CNN classification show an accuracy of 93.47%, with high precision, recall, and F1-score, at 93.55%, 93.74%, and 93.62%, respectively. Meanwhile, the CNN-SVM model with "poly," "rbf," "linear," and "sigmoid" kernels exhibits varied performances. Overall, the CNN-SVM model performs lower than the CNN model. The findings offer insights for medical image analysis and skin disease classification research. Researchers can enhance CNN-SVM model performance with varied kernel types and techniques for complex feature representations.
Comparison of Algorithms for Sentiment Analysis of Operator Satisfaction Level for Increasing Neo Feeder Applications in PDDikti Higher Education LLDIKTI Region VI Semarang Central Java M. Ulil Albab; Utami, Ema; Ariatmanto, Dhani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12907

Abstract

Sentiment analysis on the satisfaction level of PDDikti operators is very important to find out how PDDikti operators feel after the version of the academic reporting application for higher education was upgraded, namely Neo Feeeder. The increase in the version of this application causes some of the features in it to not function properly. So some academic reporting activities from tertiary institutions experience problems. As a result of this condition, the most felt impact is students, where students experience delays in graduation. Then it is necessary to evaluate through sentiment analysis from PDDikti operators to find out the response from operators and be able to provide positive suggestions to developers from the PDDikti reporting application. This study applies several classification methods for sentiment analysis at once, including the Random Forest algorithm, the Support Vector Machine algorithm, the Multinomial Naïve Bayes algorithm, the Decision Tree algorithm, and the K-Nearest Neighbor algorithm. Of the 5 methods applied, the results of their performance accuracy will be compared. The performance of the highest classification algorithm is the K-Nearest Neighbor (K-NN) algorithm which produces an accuracy value when testing data, which is up to 90% using the oversampling technique in unbalanced classes. While the lowest classification accuracy performance value is in the Multinomial Naïve Bayes (MNB) algorithm with a value of 76%. It is proven that oversampling can help the performance of the classification algorithm to be more optimal. Thus, it should be noted that the balance of data classes is an important factor when applying the classification method.
Co-Authors Adrianto M. Wijaya Ahsan, Muhammad Salimy Alfansani, Abdul Rauf Alva Hendi Muhammad Amali, Amali Anggriandi, Dendi Anjar Setiawan Anton, Tri Arfian Hendro Priyono Arfian Yogi Ferianto Arief Setyanto Aris Setiyadi Bangun Watono BHANU SRI NUGRAHA Chaedar Fatach, Muhamad Reza Darmawan, Eko Rahmad Didi Kurniawan Edy Widodo Ema Utami Eva Putriany Fadilla, Rizqi Mirza Fahma Inti Ilmawati Firdaus, M. Haikal Ghifari, Dloifur Rohman Al Hayaty, Mardhiya Jordy, Roy Karim, Hildamayanti KUSRINI Kusrini Kusrini Kusrini, Kusrini Linggama, Muhamad Nurfaizi Lukman Lukman M. Ilham Arief M. Ulil Albab Majid, Muhammad Arif Kholis Metha, Halifa Sekar Mohammad Suyanto Muhammad Nabil Muhammad Rifa'i, Anggi Muhammad Salimy Ahsan Mulia Sulistiyono Norhikmah . Nugroho, Agung Nurrizky, Akbar Nur’aini Pramudyantoro, Arvi Prayoga, Dimas Putriany, Eva Quratul Ain Ramdhani, Mohamad Dhicy Rifai, Anggi Muhammad Rosmantyo, Windhy Rokhmat Rudiyan, Ari Rusdi Rahman, Muhammad Selvi Marcellia Setiawan, Hadiguna Silvi Agustanti Bambang Slameto, Andika Agus Subandono, Anelta Tirta Putri Sukoco Sukoco Supriadi, Oki Akbar Suryono, Wachid Daga Suyanto, M Tikasni, Elisa Triesa Wea, Engel Bertus Turmudi Zy, Ahmad Wiwik Handayani Zakaria Zakaria Zuhri, Muhammad Rafli Zulpan Hadi